Remote sensing by aerial vehicles is a practice that has been around for several decades. Recent advancements in small Unmanned Aerial Systems (sUAS), paralleled with miniaturization of high quality cameras systems, has led to the development of a new platform for aerial photography and data collection. The unique flight profile of sUAS and their novelty leaves much work to be done in the area of controls research known as coverage planning. Currently the flight planning is manual and entirely dependent on an operator. Often the optimal path is counter-intuitive, either by selecting a direction for flight that is not obvious or by turning to the adjacent flightline by first turning away from it.
Precision maps acquired using aerial photography are a truly cross-industry product that is ubiquitous with the Internet age. The geospatial industry has been around in its modern form for nearly two decades, and now products like Google Maps™ and Bing™ Maps bring geospatial data to the masses in a manner never before seen. Advances in optics and sensors have increased the quality of the data product. However, the methods of acquisition have remained largely the same.
Satellites now provide frequent large scale imaging, but their timing and position are largely pre-determined. Furthermore the capital cost of developing, launching, and maintaining a satellite precludes rapid evolution of satellite-based imagery. Therefore, aerial platforms have been the method of choice for on-demand data. Small to midsized aircraft are outfitted with large cameras and sensing equipment, and are available for scheduled flights nearly world-wide [25].
Recently, a new technology has matured to the point of wide-scale adoption for this same purpose. Small Unmanned Aerial Systems (sUAS) have become reliable and robust enough to now be considered for routine use in aerial photography missions [17]. These sUAS in fact are an ideal candidate platform for small scale (sub 1000 acre) surveys because of their low operational cost, ease of deployment, and flight profile.
Emerging civilian data collection projects are utilizing military sUAS for a role they were not designed to fill. Military sUAS are designed to fill an operational gap in real time intelligence, surveillance, and reconnaissance (ISR) at the squad level. These vehicles trade robustness and ease of operations for performance, endurance, and stability. Fixed or gimbaled cameras stream live video back to a single operator, who flies the vehicle much like a video game through the forward looking camera. The first forays into using sUAS for remote sensing focused on these types of surplus military systems [24].
When military sUAS are adapted to civilian mapping missions, the design tradeoffs of the military sUAS severely limit the performance of the systems. One prominent case is the acquisition of dozens of Raven systems by the (United States Geological Service) USGS in support of their sUAS program [16]. Ravens were designed by AeroVironment in 2002 for the U.S. Army's SUAV program to complete ISR missions. These systems employ both full color and near infrared cameras, operating at 480×600 lines of (National Television System Committee) NTSC resolution. While these imaging systems may be adequate for locating a man-sized object on the move from 300 m, they are less effective at geospatial data collection.
While it is possible to collect geospatial data from real time video, it is sub-optimal [18]. Video-photography and still-photography are essentially a trade off between resolution and frame rate. Moreover, progressive scan cameras add further image loss as compared to stills. A still camera operates at very high resolution and low frame rate, while a video camera operates at a low resolution and high-frame rate. While more “pictures” will guarantee coverage, the use of real time video results in the need for extra processing and larger logistical footprint in exchange for the extra coverage. Video generates large amounts of data that can be cost prohibitive to store on board the aircraft, and, therefore, the data is typically sent back to the ground via a line-of-sight radio modem. Any break in the link between the aircraft and the recording station on the ground results in lost coverage. Furthermore, the processing of excessive image overlap is more time consuming than processing the data acquired from stills that have less image overlap.
An early UAS vehicle, the Tadpole, led to the NOVA series of aircraft. By the mid 2000s, miniaturization of all components for UAS allowed full navigation and guidance, such as in the Procerus® Kestrel™ autopilot. A still camera of sufficient resolution was placed on the aircraft to use direct-georeferencing techniques. Furthermore, batteries could power an aircraft for enough time to collect a statistically relevant sample of imagery. The NOVA 1 [23] aircraft incorporated such a still camera, batteries, and full navigation and guidance.
The NOVA 2 incorporated improved navigation and control technology in the form of a Kestrel™ v2 system, the accompanying ground station software Virtual Cockpit™ v4.0, and Ground Control Station (GCS) hardware. Wilkinson's work on the system architecture highlighted the need for proper flight planning, and this was the first system where flight planning was injected into the workflow as a critical process [24].
When discussing the motion of vehicle UAV in the air, “flightlines” are typically defined as the path, commonly linear, from one waypoint to the next. Curved flightlines can also be used. Two or more flightlines form a flight path, or the total intended path of the aircraft. The “target area” is defined as the area on the ground of interest for data collecting, such as photography, and the flight path is generally centered above the target area.
The NOVA 2 [24] had a limited flight envelope of 22 m/s for safe slow-flight. Additionally, the early payloads were only capable of taking an image every 2.5 seconds. A typical wind speed of ˜4 m/s downwind at this image rate resulted in an Airbase (B), or the distance between the centroid of pictures, of 65 m. For many missions the required B based on the desired resolution was on the order of ˜50 m, thus limiting flights to flying against the wind (upwind), lowering the B to 45 m. Therefore, wind effects led to flightplans where the target acquisition flew “upwind” only, resulting in the pattern shown in FIG. 1, which is known as a “dipole.”
These first flightplans were effective but highly inefficient. No inflight adjustments were made, and, therefore, the plans were created to account for the worst wind conditions. The vehicle traveled downwind outside of the target area, and there were no constraints on maneuvering to the next flightline.
The NOVA 2.1 is the current iteration of the NOVA series of aircraft, and like its predecessors incorporates the latest in guidance and navigation. The NOVA 2.1 features a Kestrel™ v2.23 and Virtual Cockpit™ v6.0. For the first time in the NOVA series, this system had custom software written for automated pre-flight procedures and very rudimentary flight planning. Based on the dipole patterns developed for the NOVA 2, a flightplan could be generated with simple inputs of flightline width, number of lines, length of lines, and direction. This is in no way optimal, but decreased the Concept of Operations (CONOPS) of flight planning from 45 minutes down to 10.
The performance envelope of the NOVA 2.1 also improved over the NOVA 2 platform, with the minimum safe working speed decreased from 22 m/s down to 15 m/s, and the flight time from ˜50 minutes to ˜90 minutes at an optimum 17 m/s. Furthermore, improvements in the payload brought acquisition time from 2.5 seconds down to 2.3 seconds. At an optimal flight speed, even with a 4 m/s tailwind the B is under the typical requirement of 50 m.
The NOVA series of aircraft are “all electric”, meaning an electric motor is used for propulsion. This has direct coverage planning implications because the mass of the vehicle does not decrease as the flight continues, with traditional combustion propulsion. Therefore, traditional range and efficiency calculations are invalid and speed-to-fly theory as used by Evers becomes applicable [5]. In his study, Evers concludes that modifying the airspeed of an aircraft in the presence of wind, either by increasing ground coverage and speeding up into the wind, or slowing to optimal cruise and “floating” downwind, results in an increased range of the aircraft.
The NOVA 2.1 was the first successfully deployed version of the NOVA aircraft series, and was routinely deployed to South Florida in the summer of 2010. Even with the increased efficiency of creating flightplans, it became quickly apparent that there was much to improve on the CONOPS. The improved dynamics of the vehicle, along with payload improvements, removed the constraints behind the inefficient “dipole” pattern. More traditional flightplans could now be pursued.
The Kestrel™ 2.23, when fully tuned, is capable of following a linear path with error on the same magnitude of that of lateral GPS (around 5 meters) [20]. Kestrel™ autopilots are capable of completely autonomous navigation and control, but flight planning is completely dependent on pilot-on-the-loop involvement. In other words, the aircraft is capable of flying itself with operator oversight. An extensive preflight procedure has been developed for the NOVA series of aircraft as part of the CONOPS, and part of that procedure is carefully determining where the aircraft will go in-flight to ensure coverage. The Kestrel™ series is not designed for coverage planning, but, instead, is designed for ISR missions, and, therefore, loose waypoint following.
The flight-planning portion of the Kestrel™ series utilizes only the ground station software, Virtual Cockpit™. The hardware has no capabilities to determine the aircraft's path. Seemingly autonomous functions, such as return-to-home failsafes, are simply a pre-loaded set of commands that are executed with no on board intelligence. Virtual Cockpit™ is further limited by the fact that vehicle dynamics are not considered, and only linear flight paths are allowed. This means that the operator can command a flight path that the dynamics of the vehicle will not execute on. Such a case is examined in FIG. 2, where a turn with too tight of a radius is commanded. The vehicle overshoots waypoint 3 and has trouble intercepting the flightline back to waypoint 4.
This scenario is described for farming equipment by Jin, where the local curvature between farm lines is too great for the field equipment to follow. As a result, some area in the field lines will not be covered (Jin & Tang, 2010). Analogous to this case, when the commanded local curvature is beyond what the vehicle is capable of, some area in the target area will be missed. This behavior is quickly recognized by even novice pilots, and in real world deployments, this limitation is typically accounted for by the operator attempting to compensate with a “best-guess” approach, armed with a general knowledge of vehicle dynamics, or by just observing the actual flight path of the vehicle. FIG. 3 shows the same desired flight path, but with operator compensation in the form of a “turning waypoint” labeled here as 3. With this waypoint, the vehicle reintercepts the flightline from waypoint 4 to 5 successfully. This approach is non optimal, and completely dependent on, and of, the operator.
An additional problem is in the way the Kestrel™ series executes the navigation algorithm. Waypoints have a singularly defined “radius”, that when entered, triggers the next command in the navigation script. The autopilot continuously calculates the absolute difference between its current latitude and longitude, and the latitude and longitude of the desired waypoint [20]. When that distance, or error, becomes less than the radius, it is considered to be at that waypoint.
Osborne and Rysdyk highlight the problem with this type of navigation [15]. FIG. 4A shows a tracking pattern with zero radius, where the vehicle passes 2 before turning; FIG. 4B shows an optimized radius, where the vehicle starts to turn before 2 and overshoots the next path line before aligning with the next path line; and FIG. 4C shows too much of a radius, where the vehicle starts to turn well before 2 and slowly aligns with the next path line. The Kestrel™ autopilot systems allow the operator to tune this value. However, this value is the same for all waypoints, all inbound orientations, and all wind magnitudes. Osborne suggested a correction for this by using a lookup table and variable waypoint radii based on the wind speed, magnitude, and incoming flight angle [15]. Osborne's suggestions did improve ground tracking. However, the fundamental problem still exists for paths that exceed vehicle dynamics, as is the case with the NOVA 2.1 aircraft.
The radius is optimally tuned for orthogonal turns. However, the incoming angle to the i+1 flightline makes a significant difference in the tracking performance of the aircraft as shown in FIGS. 5A-5C. Among obtuse, right, and acute incoming angles, the obtuse angle has the best performance, the right angle has some slight overshoot, and the acute angle has severe re-intercept problems. It should be noted that photogrammetric flightplans most commonly use acute and right angles, and rarely use obtuse angles.
Accordingly, there is a need in the art for a method and system to produce a flight path for sUAS's.